{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "###1. 对连续型特征，可以用哪个函数可视化其分布？（给出你最常用的一个即可），并根据代码运行结果给出示例。\n",
    "###可以用seaborn库里面的distplot进行可视化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xfe52e40508>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "#导入数据集\n",
    "df = pd.read_csv(\"E://boston_housing.csv\")  \n",
    "#可视化\n",
    "sns.distplot(df['MEDV'], bins=30, kde=True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "###2. 对两个连续型特征，可以用哪个函数得到这两个特征之间的相关性？根据代码运行结果，给出示例。\n",
    "###对于两个连续的特征，可以用DataFrame的corr()函数得到他们的相关性，用热力图显示结果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xfe52f6e388>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "#创建一个画布\n",
    "fig = plt.figure(figsize=(13,9))\n",
    "#求两个特征的相关性\n",
    "data_corr = df.corr()\n",
    "#用热力图表示相关性\n",
    "sns.heatmap(data_corr, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "###3. 如果发现特征之间有较强的相关性，在选择线性回归模型时应该采取什么措施。\n",
    "###考虑在特征层面上进行PCA降维处理，在模型层面可以加正则项。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "###4. 当采用带正则的模型以及采用随机梯度下降优化算法时，需要对输入（连续型）特征进行去量纲预处理。课程代码给出了用标准化（StandardScaler）的结果，请改成最小最大缩放（MinMaxScaler）去量纲 ，并重新训练最小二乘线性回归、岭回归、和Lasso模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#采用MinMaxScaler去量纲\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "#从原始数据中分离X和y\n",
    "y = df['MEDV']\n",
    "X = df.drop('MEDV', axis = 1)\n",
    "#修改列名\n",
    "feat_names = X.columns\n",
    "\n",
    "# 分别初始化对特征和目标的标准化器\n",
    "ss_X = MinMaxScaler()\n",
    "ss_y = MinMaxScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征以及目标值进行标准化处理\n",
    "# 对训练数据，先调用fit方法训练模型，得到模型参数,然后对训练数据和测试数据进行transform\n",
    "X = ss_X.fit_transform(X)\n",
    "y = ss_y.fit_transform(y.values.reshape(-1, 1))  # 这里必须借用values调用reshape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#访问数据值、列索引、行索引\n",
    "fe_data = pd.DataFrame(data = X, columns = feat_names, index = df.index)\n",
    "fe_data['MEDV'] = y\n",
    "# 保存特征工程的结果到文件，供机器学习模型使用\n",
    "fe_data.to_csv('FE_boston_housing_第四周基础作业.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.067815</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.314815</td>\n",
       "      <td>0.577505</td>\n",
       "      <td>0.641607</td>\n",
       "      <td>0.269203</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.208015</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.089680</td>\n",
       "      <td>0.422222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.000236</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.242302</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.172840</td>\n",
       "      <td>0.547998</td>\n",
       "      <td>0.782698</td>\n",
       "      <td>0.348962</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.104962</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.204470</td>\n",
       "      <td>0.368889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.000236</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.242302</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.172840</td>\n",
       "      <td>0.694386</td>\n",
       "      <td>0.599382</td>\n",
       "      <td>0.348962</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.104962</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.989737</td>\n",
       "      <td>0.063466</td>\n",
       "      <td>0.660000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.000293</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.063050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.150206</td>\n",
       "      <td>0.658555</td>\n",
       "      <td>0.441813</td>\n",
       "      <td>0.448545</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.066794</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.994276</td>\n",
       "      <td>0.033389</td>\n",
       "      <td>0.631111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.000705</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.063050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.150206</td>\n",
       "      <td>0.687105</td>\n",
       "      <td>0.528321</td>\n",
       "      <td>0.448545</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.066794</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.099338</td>\n",
       "      <td>0.693333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CRIM    ZN     INDUS  CHAS       NOX        RM       AGE       DIS  \\\n",
       "0  0.000000  0.18  0.067815   0.0  0.314815  0.577505  0.641607  0.269203   \n",
       "1  0.000236  0.00  0.242302   0.0  0.172840  0.547998  0.782698  0.348962   \n",
       "2  0.000236  0.00  0.242302   0.0  0.172840  0.694386  0.599382  0.348962   \n",
       "3  0.000293  0.00  0.063050   0.0  0.150206  0.658555  0.441813  0.448545   \n",
       "4  0.000705  0.00  0.063050   0.0  0.150206  0.687105  0.528321  0.448545   \n",
       "\n",
       "        RAD       TAX  PTRATIO         B     LSTAT      MEDV  \n",
       "0  0.000000  0.208015      0.3  1.000000  0.089680  0.422222  \n",
       "1  0.043478  0.104962      0.5  1.000000  0.204470  0.368889  \n",
       "2  0.043478  0.104962      0.5  0.989737  0.063466  0.660000  \n",
       "3  0.086957  0.066794      0.6  0.994276  0.033389  0.631111  \n",
       "4  0.086957  0.066794      0.6  1.000000  0.099338  0.693333  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#显示数据集前几行\n",
    "fe_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "#将matplotlib绘制的图像显示在页面里\n",
    "%matplotlib inline\n",
    "#读取做完特征工程后的数据\n",
    "df = pd.read_csv('FE_boston_housing_第四周基础作业.csv')\n",
    "\n",
    "#从原始数据中分离输入特征X和输出y\n",
    "y = df['MEDV']\n",
    "X = df.drop(['MEDV'], axis=1)\n",
    "\n",
    "#特征名称，用于后续显示权重系数对应的特征\n",
    "feat_names = X.columns\n",
    "\n",
    "#将数据分割训练数据和测试数据\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#随机采样20%的数据构建测试样本，其余作为训练样本\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LinearRegression on test is 0.690295955089836\n",
      "The r2 score of LinearRegression on train is 0.7451448367308489\n"
     ]
    }
   ],
   "source": [
    "#训练最小二乘\n",
    "from sklearn.linear_model import LinearRegression\n",
    "# 1.使用默认配置初始化学习器实例\n",
    "lr = LinearRegression()\n",
    "\n",
    "# 2.用训练数据训练模型参数\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "# 3.用训练好的模型对测试集进行预测\n",
    "y_test_pred_lr = lr.predict(X_test)\n",
    "y_train_pred_lr = lr.predict(X_train)\n",
    "\n",
    "# 4.使用r2_score评价模型在测试集和训练集上的性能，并输出评价结果\n",
    "from sklearn.metrics import r2_score\n",
    "#测试集\n",
    "print(\"The r2 score of LinearRegression on test is\", r2_score(y_test, y_test_pred_lr))\n",
    "#训练集\n",
    "print(\"The r2 score of LinearRegression on train is\", r2_score(y_train, y_train_pred_lr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of RidgeCV on test is 0.6929146520257187\n",
      "The r2 score of RidgeCV on train is 0.7450810988477304\n"
     ]
    }
   ],
   "source": [
    "#训练岭回归\n",
    "from sklearn.linear_model import RidgeCV\n",
    "\n",
    "# 1.设置超参数（正则参数）范围\n",
    "# alphas = [0.01, 0.1, 1, 10, 100]\n",
    "\n",
    "# 2.生成一个RidgeCV实例\n",
    "# ridge = RidgeCV(alphas=alphas, store_cv_values=True)\n",
    "ridge = RidgeCV()\n",
    "\n",
    "# 3.训练模型\n",
    "ridge.fit(X_train, y_train)\n",
    "\n",
    "# 4.预测\n",
    "y_test_pred_ridge = ridge.predict(X_test)\n",
    "y_train_pred_ridge = ridge.predict(X_train)\n",
    "\n",
    "# 5.使用r2_score评价模型在测试集和训练集上的性能，并输出评价结果\n",
    "# from sklearn.metrics import r2_score\n",
    "#测试集\n",
    "print(\"The r2 score of RidgeCV on test is\", r2_score(y_test, y_test_pred_ridge))\n",
    "#训练集\n",
    "print(\"The r2 score of RidgeCV on train is\", r2_score(y_train, y_train_pred_ridge))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LassoCV on test is 0.6909712241255916\n",
      "The r2 score of LassoCV on train is 0.7451229340174219\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "#训练Lasso模型\n",
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "# 1.设置超参数（正则参数）范围\n",
    "# alphas = [0.01, 0.1, 1, 10, 100]\n",
    "\n",
    "# 2.生成一个RidgeCV实例\n",
    "# lasso = LassoCV(alphas=alphas)\n",
    "lasso = LassoCV()\n",
    "\n",
    "# 3.训练模型\n",
    "lasso.fit(X_train, y_train)\n",
    "\n",
    "# 4.预测\n",
    "y_test_pred_lasso = lasso.predict(X_test)\n",
    "y_train_pred_lasso = lasso.predict(X_train)\n",
    "\n",
    "# 5.使用r2_score评价模型在测试集和训练集上的性能，并输出评价结果\n",
    "# from sklearn.metrics import r2_score\n",
    "#测试集\n",
    "print(\"The r2 score of LassoCV on test is\", r2_score(y_test, y_test_pred_lasso))\n",
    "#训练集\n",
    "print(\"The r2 score of LassoCV on train is\", r2_score(y_train, y_train_pred_lasso))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#5. 代码中给出了岭回归（RidgeCV）和Lasso（LassoCV）超参数（alpha_）调优的过程，请结合两个最佳模型以及最小二乘线性回归模型的结果，给出什么场合应该用岭回归，什么场合用Lasso，什么场合用最小二乘。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#最小二乘线性回归的解(XTX)−1XTY，需要对矩阵XTX求逆。当输入特征存在共线性（某些特征可以用其他特征的线性组合表示），矩阵X是接近不满秩，矩阵XTX接近奇异，求逆不稳定，所以结果不会很理想。所以最小二乘线性回归适用于特征之间不存在共线性的情况。\n",
    "#岭回归由于加入了L2正则项，通过alpha参数的调节，可以平衡拟合程度和模型复杂度之间的关系，岭回归的解为：(XTX+λI)−1XTY即使在输入特征存在共线性，矩阵X是接近不满秩，矩阵XTX对角线存在等于0或接近于0的元素，但0+λ≠0，(XTX+λI)求逆仍可得到稳定解。因此岭回归适用于输入特征存在共线性的情况。\n",
    "#Lasso回归中的L1正则同岭回归的L2正则一样是用来平衡拟合程度和模型复杂度的，但当正则参数取合适值时，L1正则能使有些线性回归系数为0,得到稀疏模型。所以，Lasso回归模型适用于，当输入特征多，有些特征与目标变量之间相关性很弱时，L1正则可能只选择强相关的特征，模型解释性好。"
   ]
  }
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